Biology subjects

Greener, J. G.

Publications and source records attributed to Greener, J. G..

7 recordsLinked to original sources

bioRxiv2024

Molecular mechanism of Mad2 conformational conversion promoted by the Mad2-interaction motif of Cdc20

During mitosis, unattached kinetochores trigger the spindle assembly checkpoint by promoting assembly of the mitotic checkpoint complex, a heterotetramer comprising Mad2, Cdc20, BubR1 and Bub3. Critical to this process is the kinetochore-mediated catalysis of an intrinsically slow conformational conversion of Mad2 from an open (O-Mad2) inactive state to a closed (C-Mad2) active state bound to Cdc20. These Mad2 conformational changes involve substantial remodelling of the N-terminal {beta}1 strand and C-terminal {beta}7/{beta}8 hairpin. In vitro, the Mad2- interaction motif (MIM) of Cdc20 (Cdc20MIM) triggers rapid conversion of O- to C-Mad2, effectively removing the kinetic barrier for MCC assembly. How Cdc20MIM directly induces Mad2 conversion remains unclear. In this study we demonstrate that the Cdc20MIM-binding site is inaccessible in O-Mad2. Time-resolved NMR and molecular dynamics simulations show how Mad2 conversion involves sequential conformational changes of flexible structural elements in O-Mad2, orchestrated by Cdc20MIM. Conversion is initiated by the {beta}7/{beta}8 hairpin of O-Mad2 transiently unfolding to expose a nascent Cdc20MIM-binding site. Engagement of Cdc20MIM to this site promotes release of the {beta}1 strand. We propose that initial conformational changes of the {beta}7/{beta}8 hairpin allows binding of Cdc20MIM to a transient intermediate state of Mad2, thereby lowering the kinetic barrier to Mad2 conversion.

biochemistry↗

bioRxiv2023

Differentiable simulation to develop molecular dynamics force fields for disordered proteins

Implicit solvent force fields are computationally efficient but can be unsuitable for running molecular dynamics on disordered proteins. Here I improve the a99SB-disp force field and the GBNeck2 implicit solvent model to better describe disordered proteins. Differentiable molecular simulations with 5 ns trajectories are used to jointly optimise 108 parameters to better match explicit solvent trajectories. Simulations with the improved force field better reproduce the radius of gyration and secondary structure content seen in experiments, whilst showing slightly degraded performance on folded proteins and protein complexes. The force field, called GB99dms, reproduces the results of a small molecule binding study and improves agreement to experiment for the aggregation of amyloid peptides. GB99dms, which can be used in OpenMM, is available at https://github.com/greener-group/GB99dms. This work is the first to show that gradients can be obtained directly from nanosecond-length differentiable simulations of biomolecules and highlights the effectiveness of this approach to training whole force fields to match desired properties.

biophysics↗

bioRxiv2022

Fast protein structure searching using structure graph embeddings

Comparing and searching protein structures independent of primary sequence has proved useful for remote homology detection, function annotation and protein classification. Fast and accurate methods to search with structures will be essential to make use of the vast databases that have recently become available, in the same way that fast protein sequence searching underpins much of bioinformatics. We train a simple graph neural network using supervised contrastive learning to learn a low-dimensional embedding of protein structure. The method, called Progres, is available as software at https://github.com/greener-group/progres and as a web server at https://progres.mrc-lmb.cam.ac.uk. It has accuracy comparable to the best current methods and can search the AlphaFold database TED domains in a tenth of a second per query on CPU.

bioinformatics↗

bioRxiv2021

Using AlphaFold for Rapid and Accurate Fixed Backbone Protein Design

AO_SCPLOWBSTRACTC_SCPLOWThe prediction of protein structure and the design of novel protein sequences and structures have long been intertwined. The recently released AlphaFold has heralded a new generation of accurate protein structure prediction, but the extent to which this affects protein design stands yet unexplored. Here we develop a rapid and effective approach for fixed backbone computational protein design, leveraging the predictive power of AlphaFold. For several designs we demonstrate that not only are the AlphaFold predicted structures in agreement with the desired backbones, but they are also supported by the structure predictions of other supervised methods as well as ab initio folding. These results suggest that AlphaFold, and methods like it, are able to facilitate the development of a new range of novel and accurate protein design methodologies.

bioinformatics↗

bioRxiv2021

Differentiable molecular simulation can learn all the parameters in a coarse-grained force field for proteins

Finding optimal parameters for force fields used in molecular simulation is a challenging and time-consuming task, partly due to the difficulty of tuning multiple parameters at once. Automatic differentiation presents a general solution: run a simulation, obtain gradients of a loss function with respect to all the parameters, and use these to improve the force field. This approach takes advantage of the deep learning revolution whilst retaining the interpretability and efficiency of existing force fields. We demonstrate that this is possible by parameterising a simple coarse-grained force field for proteins, based on training simulations of up to 2,000 steps learning to keep the native structure stable. The learned potential matches chemical knowledge and PDB data, can fold and reproduce the dynamics of small proteins, and shows ability in protein design and model scoring applications. Problems in applying differentiable molecular simulation to all-atom models of proteins are discussed along with possible solutions. The learned potential, simulation scripts and training code are made available at https://github.com/psipred/cgdms.

bioinformatics↗

bioRxiv2020

Deep learning-based prediction of protein structure using learned representations of multiple sequence alignments

Deep learning-based prediction of protein structure usually begins by constructing a multiple sequence alignment (MSA) containing homologues of the target protein. The most successful approaches combine large feature sets derived from MSAs, and considerable computational effort is spent deriving these input features. We present a method that greatly reduces the amount of preprocessing required for a target MSA, while producing main chain coordinates as a direct output of a deep neural network. The network makes use of just three recurrent networks and a stack of residual convolutional layers, making the predictor very fast to run, and easy to install and use. Our approach constructs a directly learned representation of the sequences in an MSA, starting from a one-hot encoding of the sequences. When supplemented with an approximate precision matrix, the learned representation can be used to produce structural models of comparable or greater accuracy as compared to our original DMPfold method, while requiring less than a second to produce a typical model. This level of accuracy and speed allows very large-scale 3-D modelling of proteins on minimal hardware, and we demonstrate that by producing models for over 1.3 million uncharacterized regions of proteins extracted from the BFD sequence clusters. After constructing an initial set of approximate models, we select a confident subset of over 30,000 models for further refinement and analysis, revealing putative novel protein folds. We also provide updated models for over 5,000 Pfam families studied in the original DMPfold paper. Significance StatementWe present a deep learning-based predictor of protein tertiary structure that uses only a multiple sequence alignment (MSA) as input. To date, most emphasis has been on the accuracy of such deep learning methods, but here we show that accurate structure prediction is also possible in very short timeframes (a few hundred milliseconds). In our method, the backbone coordinates of the target protein are output directly from the neural network, which makes the predictor extremely fast. As a demonstration, we generated over 1.3 million models of uncharacterised proteins in the BFD, a large sequence database including many metagenomic sequences. Our results showcase the utility of ultrafast and accurate tertiary structure prediction in rapidly exploring the "dark space" of proteins.

bioinformatics↗

bioRxiv2019

Prediction of inter-residue contacts with DeepMetaPSICOV in CASP13

In this article, we describe our efforts in contact prediction in the CASP13 experiment. We employed a new deep learning-based contact prediction tool, DeepMetaPSICOV (or DMP for short), together with new methods and data sources for alignment generation. DMP evolved from MetaPSICOV and DeepCov and combines the input feature sets used by these methods as input to a deep, fully convolutional residual neural network. We also improved our method for multiple sequence alignment generation and included metagenomic sequences in the search. We discuss successes and failures of our approach and identify areas where further improvements may be possible. DMP is freely available at: https://github.com/psipred/DeepMetaPSICOV.

bioinformatics↗